Thesis Directors: Prof. T. Schneider, Prof. J. M. Kolinski
\r\nMechanics doctoral program
\r\nThesis Nr. 11458
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\r\nTo take part in the public defense, please contact directly the speaker
Thesis Directors: Prof. A. Buffa, Dr E. Sande
\r\nMathematics doctoral program
\r\nThesis Nr. 11727
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\r\nTo take part in the public defense, please contact directly the speaker
To promote research and education in cyber-defence, the EPFL and the Cyber-Defence (CYD) Campus have jointly launched the “CYD Fellowships – A Talent Program for Cyber-Defence Research.”
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\r\nThe 14th call for applications is now open with a rolling call for Master Thesis Fellowship and Proof of Concept Fellowship applications, and with a deadline of 19 August 2026 (17:00 CEST) for Doctoral and Distinguished Postdoctoral Fellowship applications.
\r\nEPFL and the CYD Campus organise an online applicant workshop dedicated to the CYD Fellowships 14th call for applications.
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\r\nThe workshop will target researchers (master, doctoral, postdoctoral level) and entrepreneurs who are willing to engage with cross-cutting issues in cyber-defence, with a particular emphasis on:\r\n
The symposium is organized by the AI Models of Biomolecules Focus Group of the SIB Swiss Institute of Bioinformatics in collaboration with the University of Bern.
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\r\nThe next edition of our AI Meets RNA Biology Mini-Symposium, is taking place on Tuesday, June 16, 2026, in a hybrid format at the EPFL AI Center (Lausanne) and online.
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\r\nThis event brings together researchers interested in the latest advances in deep learning for biomolecules, with a particular focus on RNA structure prediction, translational regulation, and computational approaches at the intersection of AI and RNA biology.
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\r\nProgram\r\n
The EPFL AI Center is delighted to invite you to join the exchange session about the Research Computing Platform (RCP).
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\r\nAre you building, training ML models? Are you currently facing challenges regarding IT infrastructure, and looking for reliable, collaborative, high-capacity, and affordable storage solutions to manage your research data? Are you seeking information about the computing costs?
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\r\nThe Research Computing Platform (RCP) offers campus-wide IT infrastructure geared for the research community. The offering includes a GPU infrastructure (> 400) aiming at making AI compute as accessible as possible throughout the campus.
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\r\nThis event will provide insights into the various services RCP offers to the EPFL research community.
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\r\nEvent Details:\r\n
The talk is jointly organized by the EPFL AI Center and the DLAB as part of the AI fundamentals seminar series.
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\r\nHosting professor: Prof. Robert West (DLAB)
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\r\nTitle
\r\nBenchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas
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\r\nAbstract
\r\nIt is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite trend: LLMs with stronger reasoning capabilities behave _less_ cooperatively in mixed-motive games such as the prisoner's dilemma and public goods settings. Indeed, our experiments show that recent models---with or without reasoning enabled---consistently defect in single-shot social dilemmas.
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\r\nTo tackle this safety concern, we present the first comparative study of game-theoretic mechanisms designed to enable cooperative outcomes between rational agents _in equilibrium_. Across four social dilemmas testing distinct components of robust cooperation, we evaluate four families of mechanisms: (1) repeating the game for many rounds, (2) reputation systems, (3) third-party mediators to delegate decision making to, and (4) contract agreements for outcome-conditional payments between players. Among our findings, we establish that contracting and mediation are most effective in achieving cooperative outcomes between capable LLM models, and that repetition-induced cooperation deteriorates drastically when co-players vary. Moreover, we demonstrate that the mechanisms become _more effective_ under evolutionary pressures to maximize individual payoffs.
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\r\nBio
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\r\nEmanuel Tewolde is a fourth-year Computer Science PhD student at Carnegie Mellon University advised by Vincent Conitzer. His research focuses on algorithmic game theory, reinforcement learning, and LLM agents, with an emphasis on the safety, coordination, cooperation, and alignment of AI systems. Ultimately, he strives to understand how to enable artificial intelligence and humans to effectively achieve better social outcomes in strategic interactions.
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\r\nEmanuel’s work is supported by the Cooperative AI PhD Fellowship, and has previously received the AAAI 2025 Best Poster Award. He has previously worked on AI research agents with Meta FAIR, and researched ML methods for renewable energy systems with the Fraunhofer-Gesellschaft. Prior to CMU, he completed Master’s and Bachelor’s degrees in Mathematics at Imperial College London and the Technical University of Darmstadt respectively.
The talk is jointly organized by the EPFL AI Center and the EPFL Signal Processing Laboratory (LTS5) as part of the AI Fundamentals seminar series.
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\r\nHost: Prof. Jean-Philippe Thiran
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\r\nTitle
\r\nDeep Learning Solution of the Eigenvalue Problem for Differential Operators
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\r\nAbstract
\r\nSolving the eigenvalue problem for differential operators is a common problem in many scientific fields. We introduce a novel Neural Network (NN) -based solver for the eigenvalue problem of differential self-adjoint operators, where the eigenpairs are learned in an unsupervised end-to-end fashion. We propose several training procedures for solving increasingly challenging tasks towards the general eigenvalue problem. The proposed solver is capable of finding the M smallest eigenpairs for a general linear differential operator. Our proposed method simultaneously solves several eigenpairs and can be easily used on free-form domains. We exemplify it on L-shape and circular cut domains. A significant contribution of this
\r\nwork is an analysis of the numerical error of this method. In particular an upper bound for the (unknown) solution error is given in terms of the (measured) truncation error of the PDE and the network structure.
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\r\nBio
\r\nNir Sochen, finished his B.Sc studies in Physics 1986 and his M.Sc. studies in theoretical physics 1988 both in the University of Tel-Aviv. Received his PhD in Theoretical physics 1992 from the Universit\\’e de Paris-Sud while conducting his research in the Service de Physique Th\\’eorique at the Centre d’Etude Nucleaire at Saclay, France. He continued with a one year research in the Ecole Normale Superieure in Paris on the Haute Etude Scientifique fellowship, and a three years NSF fellowship in the physics department of the University of California at Berkeley. It is in Berkeley that his interests shifted from quantum field theories and integrable models, related to high-energy physics and string theory, to computer vision and image processing. He spent one year in the physics department at the University of Tel-Aviv and two years in the Faculty of Electrical Engineering in the Technion-Israel Institute of Technology. Currently he is a senior lecturer in the Department of Applied Mathematics, University of Tel-Aviv. He is also a member of the Ollendorf center in the Technion.
\r\nHis main research interests are the applications of differential geometry and statistical physics in image processing and computational vision.
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